[In silico Screening of Protein-Protein Interaction Modulators Using the p53 and 14-3-3y Proteins as an Example]

Area of Science:

• Biochemistry
• Molecular Biology
• Computational Biology

Context:

• The p53 protein is crucial in preventing cancer, with mutations in its gene (TP53) found in about 50% of human cancers.
• Understanding p53's interactions is key to deciphering its role in tumorigenesis and cancer progression.
• p53 is involved in DNA damage signaling but presents structural challenges due to unstructured regions.

Purpose:

• To investigate the interaction between the p53 protein and 14-3-3γ monomers using in silico approaches.
• To identify small molecule compounds capable of modulating the p53-14-3-3γ interaction.

Summary:

• Employed tertiary structure modeling, molecular dynamics, molecular docking, and virtual ligand screening.
• Generated full-length in silico models of both 14-3-3γ and p53.
• Identified a specific ligand binding pocket at the p53-14-3-3γ interaction interface.
• Selected potential protein-protein modulators exhibiting high binding affinity.

Impact:

• Provides novel insights into the molecular mechanisms governing p53 and 14-3-3γ interactions.
• Identifies potential therapeutic targets for modulating p53 function in cancer.
• Demonstrates the utility of computational methods in drug discovery for protein-protein interactions.